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modules.py
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modules.py
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import numpy as np
import torch
import torch.nn as nn
class Conv_1d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=1, pooling=2):
super(Conv_1d, self).__init__()
self.conv = nn.Conv1d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn = nn.BatchNorm1d(output_channels)
self.relu = nn.ReLU()
self.mp = nn.MaxPool1d(pooling)
def forward(self, x):
out = self.mp(self.relu(self.bn(self.conv(x))))
return out
class Conv_2d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=1, pooling=2):
super(Conv_2d, self).__init__()
self.conv = nn.Conv2d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn = nn.BatchNorm2d(output_channels)
self.relu = nn.ReLU()
self.mp = nn.MaxPool2d(pooling)
def forward(self, x):
out = self.mp(self.relu(self.bn(self.conv(x))))
return out
class Res_2d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=2):
super(Res_2d, self).__init__()
# convolution
self.conv_1 = nn.Conv2d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn_1 = nn.BatchNorm2d(output_channels)
self.conv_2 = nn.Conv2d(output_channels, output_channels, shape, padding=shape//2)
self.bn_2 = nn.BatchNorm2d(output_channels)
# residual
self.diff = False
if (stride != 1) or (input_channels != output_channels):
self.conv_3 = nn.Conv2d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn_3 = nn.BatchNorm2d(output_channels)
self.diff = True
self.relu = nn.ReLU()
def forward(self, x):
# convolution
out = self.bn_2(self.conv_2(self.relu(self.bn_1(self.conv_1(x)))))
# residual
if self.diff:
x = self.bn_3(self.conv_3(x))
out = x + out
out = self.relu(out)
return out
class Res_2d_mp(nn.Module):
def __init__(self, input_channels, output_channels, pooling=2):
super(Res_2d_mp, self).__init__()
self.conv_1 = nn.Conv2d(input_channels, output_channels, 3, padding=1)
self.bn_1 = nn.BatchNorm2d(output_channels)
self.conv_2 = nn.Conv2d(output_channels, output_channels, 3, padding=1)
self.bn_2 = nn.BatchNorm2d(output_channels)
self.relu = nn.ReLU()
self.mp = nn.MaxPool2d(pooling)
def forward(self, x):
out = self.bn_2(self.conv_2(self.relu(self.bn_1(self.conv_1(x)))))
out = x + out
out = self.mp(self.relu(out))
return out
class ResSE_1d(nn.Module):
def __init__(self, input_channels, output_channels, shape=3, stride=1, pooling=3):
super(ResSE_1d, self).__init__()
# convolution
self.conv_1 = nn.Conv1d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn_1 = nn.BatchNorm1d(output_channels)
self.conv_2 = nn.Conv1d(output_channels, output_channels, shape, padding=shape//2)
self.bn_2 = nn.BatchNorm1d(output_channels)
# squeeze & excitation
self.dense1 = nn.Linear(output_channels, output_channels)
self.dense2 = nn.Linear(output_channels, output_channels)
# residual
self.diff = False
if (stride != 1) or (input_channels != output_channels):
self.conv_3 = nn.Conv1d(input_channels, output_channels, shape, stride=stride, padding=shape//2)
self.bn_3 = nn.BatchNorm1d(output_channels)
self.diff = True
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.mp = nn.MaxPool1d(pooling)
def forward(self, x):
# convolution
out = self.bn_2(self.conv_2(self.relu(self.bn_1(self.conv_1(x)))))
# squeeze & excitation
se_out = nn.AvgPool1d(out.size(-1))(out)
se_out = se_out.squeeze(-1)
se_out = self.relu(self.dense1(se_out))
se_out = self.sigmoid(self.dense2(se_out))
se_out = se_out.unsqueeze(-1)
out = torch.mul(out, se_out)
# residual
if self.diff:
x = self.bn_3(self.conv_3(x))
out = x + out
out = self.mp(self.relu(out))
return out
class Conv_V(nn.Module):
# vertical convolution
def __init__(self, input_channels, output_channels, filter_shape):
super(Conv_V, self).__init__()
self.conv = nn.Conv2d(input_channels, output_channels, filter_shape,
padding=(0, filter_shape[1]//2))
self.bn = nn.BatchNorm2d(output_channels)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.bn(self.conv(x)))
freq = x.size(2)
out = nn.MaxPool2d((freq, 1), stride=(freq, 1))(x)
out = out.squeeze(2)
return out
class Conv_H(nn.Module):
# horizontal convolution
def __init__(self, input_channels, output_channels, filter_length):
super(Conv_H, self).__init__()
self.conv = nn.Conv1d(input_channels, output_channels, filter_length,
padding=filter_length//2)
self.bn = nn.BatchNorm1d(output_channels)
self.relu = nn.ReLU()
def forward(self, x):
freq = x.size(2)
out = nn.AvgPool2d((freq, 1), stride=(freq, 1))(x)
out = out.squeeze(2)
out = self.relu(self.bn(self.conv(out)))
return out
# Modules for harmonic filters
def hz_to_midi(hz):
return 12 * (torch.log2(hz) - np.log2(440.0)) + 69
def midi_to_hz(midi):
return 440.0 * (2.0 ** ((midi - 69.0)/12.0))